Describe participants behavior in a study. (Mean & Standard Deviation.)
Numbers that summarize and describe the behavior of participants in a study; the mean & standard deviation are descriptive statistics, for example.
Effect Size (p. 50)
Strength of relationships between 2 or more variables.
The strength of the relationship between two or more variables, usually expressed as the proportion of variance in one variable that can be accounted for by another variable.
Error Variance (p. 45)
Portion of total variance unaccounted for after systematic variance is removed.
Variance that is unrelated to variables in a study.
Inferential Statistics
Mathematical analyses that allow researchers to draw conclusions regarding the reliability and generalizability of their data.
(T-tests and F-tests)
Mean (p. 40)
The mathematical average of a set of scores.
The sum of a set of scored divided by the number of scores.
Measures of Strenght of Association (p. 47)
Describes strength of relationship between variables.
Descriptive statistics that convey information about the strength of the relationship between variables;
Meta-analysis (p. 50)
Statistical procedure that analyzes and integrates results of many studies on a single topic.
Range (p. 39)
A measure of variability that is equal to the difference between the largest & smallest scores in a set of data.
Statistical Notation (p. 42)
A system of symbols that represents particular mathematical operations, variables, and statistics.
Example: x(bar) = mean, E = sum/add, and s^2 = variance.
Systematic Variance (p. 43)
The portion of total variance that is related directly to variables being investigated.
The portion of the total variance in a set of scores that is related in an orderly, predictable fashion to the variables the researcher is investigating.
Total Sum of Squares (p. 42)
The total variability in a set of data.
Calculated by subtracting the mean from each score, squaring the differences, and summing them.
Total Variance (p. 43)
(The total sum of squares)/(# of scores) - (1)
The total sum of squares divided by the number of scores minus 1.
Variability (p. 36)
The degree to which scores differ.
The degree to which scores in a set of data differ or vary from one another.
Variance (p. 39)
The variability in a set of data.
A numerical index of the variability in a set of data.